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Background: Widespread dementia detection could increase clinical trial candidates and enable appropriate interventions. Since the Clock Drawing Test (CDT) can be potentially used for diagnosing dementia-related disorders, it can be leveraged to develop a computer-aided screening tool. Objective: To evaluate if a machine learning model that uses images from the CDT can predict mild cognitive impairment or dementia. Methods: Images of an analog clock drawn by 3,263 cognitively intact and 160 impaired subjects were collected during in-person dementia evaluations by the Framingham Heart Study. We processed the CDT images, participant’s age, and education level using a deep learning algorithm to predict dementia status. Results: When only the CDT images were used, the deep learning model predicted dementia status with an area under the receiver operating characteristic curve (AUC) of 81.3% ± 4.3%. A composite logistic regression model using age, level of education, and the predictions from the CDT-only model, yielded an average AUC and average F1 score of 91.9% ±1.1% and 94.6% ±0.4%, respectively. Conclusion: Our modeling framework establishes a proof-of-principle that deep learning can be applied on images derived from the CDT to predict dementia status. When fully validated, this approach can offer a cost-effective and easily deployable mechanism for detecting cognitive impairment.
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Phenotyping Cognitive Impairment using Graphomotor and Latency Features in Digital Clock Drawing Test*
The Clock Drawing Test, where the participant is asked to draw a clock from memory and copy a model clock, is widely used for screening of cognitive impairment. The digital version of the clock test, the digital clock drawing test (dCDT), employs accelerometer and pressure sensors of a digital pen to capture time and pressure information from a participant's performance in a granular digital format. While visual features of the clock drawing test have previously been studied, little is known about the relationship between demographic and cognitive impairment characteristics with dCDT latency and graphomotor features. Here, we examine dCDT feature clusters with respect to sociodemographic and cognitive impairment outcomes. Our results show that the clusters are not significantly different in terms of age and gender, but did significantly differ in terms of education, Mini-Mental State Exam scores, and cognitive impairment diagnoses.This study shows that features extracted from digital clock drawings can provide important information regarding cognitive reserve and cognitive impairments.
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- Award ID(s):
- 1750192
- PAR ID:
- 10213927
- Date Published:
- Journal Name:
- 2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC)
- Page Range / eLocation ID:
- 5657 to 5660
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Bondi, Mark (Ed.)Background: Advantages of digital clock drawing metrics for dementia subtype classification needs examination. Objective: To assess how well kinematic, time-based, and visuospatial features extracted from the digital Clock Drawing Test (dCDT) can classify a combined group of Alzheimer’s disease/Vascular Dementia patients versus healthy controls (HC), and classify dementia patients with Alzheimer’s disease (AD) versus vascular dementia (VaD). Methods: Healthy, community-dwelling control participants (n = 175), patients diagnosed clinically with Alzheimer’s disease (n = 29), and vascular dementia (n = 27) completed the dCDT to command and copy clock drawing conditions. Thirty-seven dCDT command and 37 copy dCDT features were extracted and used with Random Forest classification models. Results: When HC participants were compared to participants with dementia, optimal area under the curve was achieved using models that combined both command and copy dCDT features (AUC = 91.52%). Similarly, when AD versus VaD participants were compared, optimal area under the curve was, achieved with models that combined both command and copy features (AUC = 76.94%). Subsequent follow-up analyses of a corpus of 10 variables of interest determined using a Gini Index found that groups could be dissociated based on kinematic, time-based, and visuospatial features. Conclusion: The dCDT is able to operationally define graphomotor output that cannot be measured using traditional paper and pencil test administration in older health controls and participants with dementia. These data suggest that kinematic, time-based, and visuospatial behavior obtained using the dCDT may provide additional neurocognitive biomarkers that may be able to identify and tract dementia syndromes.more » « less
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/EMBC44109.2020.9176469
